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5a14c00 ff63d1b 5a14c00 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | from PIL import Image
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms, models
IMG_MEAN = [0.485, 0.456, 0.406]
IMG_STD = [0.229, 0.224, 0.225]
CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073]
CLIP_STD = [0.26862954, 0.26130258, 0.27577711]
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
LR = 5e-4
CROP_SIZE = 128
RESIZE = 224
NUM_EPOCHS = 200
NUM_CROPS = 64
PATCH_THRESHOLD = 0.7
L_TV = 2e-3
L_PATCH = 9000
L_DIR = 500
L_CONTENT = 150
IMG_SIZE = 512
# copied from openai clip
IMAGENET_TEMPLATES = [
"a bad photo of a {}.",
"a photo of many {}.",
"a sculpture of a {}.",
"a photo of the hard to see {}.",
"a low resolution photo of the {}.",
"a rendering of a {}.",
"graffiti of a {}.",
"a bad photo of the {}.",
"a cropped photo of the {}.",
"a tattoo of a {}.",
"the embroidered {}.",
"a photo of a hard to see {}.",
"a bright photo of a {}.",
"a photo of a clean {}.",
"a photo of a dirty {}.",
"a dark photo of the {}.",
"a drawing of a {}.",
"a photo of my {}.",
"the plastic {}.",
"a photo of the cool {}.",
"a close-up photo of a {}.",
"a black and white photo of the {}.",
"a painting of the {}.",
"a painting of a {}.",
"a pixelated photo of the {}.",
"a sculpture of the {}.",
"a bright photo of the {}.",
"a cropped photo of a {}.",
"a plastic {}.",
"a photo of the dirty {}.",
"a jpeg corrupted photo of a {}.",
"a blurry photo of the {}.",
"a photo of the {}.",
"a good photo of the {}.",
"a rendering of the {}.",
"a {} in a video game.",
"a photo of one {}.",
"a doodle of a {}.",
"a close-up photo of the {}.",
"a photo of a {}.",
"the origami {}.",
"the {} in a video game.",
"a sketch of a {}.",
"a doodle of the {}.",
"a origami {}.",
"a low resolution photo of a {}.",
"the toy {}.",
"a rendition of the {}.",
"a photo of the clean {}.",
"a photo of a large {}.",
"a rendition of a {}.",
"a photo of a nice {}.",
"a photo of a weird {}.",
"a blurry photo of a {}.",
"a cartoon {}.",
"art of a {}.",
"a sketch of the {}.",
"a embroidered {}.",
"a pixelated photo of a {}.",
"itap of the {}.",
"a jpeg corrupted photo of the {}.",
"a good photo of a {}.",
"a plushie {}.",
"a photo of the nice {}.",
"a photo of the small {}.",
"a photo of the weird {}.",
"the cartoon {}.",
"art of the {}.",
"a drawing of the {}.",
"a photo of the large {}.",
"a black and white photo of a {}.",
"the plushie {}.",
"a dark photo of a {}.",
"itap of a {}.",
"graffiti of the {}.",
"a toy {}.",
"itap of my {}.",
"a photo of a cool {}.",
"a photo of a small {}.",
"a tattoo of the {}.",
]
def get_mean(mean_dist):
mean = torch.tensor(mean_dist).to(DEVICE)
return mean.view(1, -1, 1, 1)
def get_std(std_dist):
std = torch.tensor(std_dist).to(DEVICE)
return std.view(1, -1, 1, 1)
def normalize(data):
mean = get_mean(IMG_MEAN)
std = get_std(IMG_STD)
norm_data = (data - mean) / std
return norm_data
def clip_normalize(data):
resized = nn.functional.interpolate(data, size=RESIZE, mode='bicubic')
mean = get_mean(CLIP_MEAN)
std = get_std(CLIP_STD)
norm_data = (resized - mean) / std
return norm_data
def load_image(image):
image = image.resize((IMG_SIZE, IMG_SIZE))
transform = transforms.Compose([transforms.ToTensor()])
return transform(image)[:3, :, :].unsqueeze(0)
def get_features(image, vgg19):
# uses vgg19 model to extract content features
layers = {'0': 'conv1_1',
'5': 'conv2_1',
'10': 'conv3_1',
'19': 'conv4_1',
'21': 'conv4_2',
'28': 'conv5_1',
'31': 'conv5_2'
}
features = {}
x = image
for name, layer in vgg19._modules.items():
x = layer(x)
if name in layers:
features[layers[name]] = x
return features
def prompt_ensemble(prompt):
return [template.format(prompt) for template in IMAGENET_TEMPLATES]
def get_image_prior_losses(target):
diff1 = target[:, :, :, :-1] - target[:, :, :, 1:]
diff2 = target[:, :, :-1, :] - target[:, :, 1:, :]
diff3 = target[:, :, 1:, :-1] - target[:, :, :-1, 1:]
diff4 = target[:, :, :-1, :-1] - target[:, :, 1:, 1:]
loss_var_l2 = torch.norm(diff1) + torch.norm(diff2) + torch.norm(diff3) + torch.norm(diff4)
return loss_var_l2
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